6D pose labeling method and system and storage medium

Abstract

The invention discloses a 6D pose labeling method and system and a storage medium. The method comprises the following steps: acquiring image information of a target object; converting the target object depth picture into a first point cloud set according to the target mask; performing rough matching on the first point cloud set and a preset model point cloud set by adopting a PPF algorithm to obtain a first pose of the target object, and converting the first point cloud set into a second point cloud set according to the first pose; performing first fine matching on the second point cloud set and the model point cloud set by adopting a point-to-surface ICP algorithm to obtain a second pose of the target object, and converting the second point cloud set into a third point cloud set according to the second pose; performing secondary fine matching on the third point cloud set and the model point cloud set by adopting a point-to-point ICP algorithm to obtain a third pose of the target object; and calculating the target pose of the target object according to the obtained three poses. According to the invention, the relatively accurate target pose of the target object can be efficiently obtained.

Description

technical field [0001] The present application relates to the technical field of machine vision, in particular to a 6D pose labeling method, system and storage medium. Background technique [0002] 6D pose prediction is a key technology in 3D vision. Pose is position and attitude. The position can be described by space coordinates (x, y, z), that is, translation, and the attitude can be rotated in three directions (pitch, yaw, roll). To describe, so the pose of the object can be described by the data of 6 degrees of freedom. 6D pose prediction aims to accurately measure the 3D rotation value and 3D translation value of the target object, and is widely used in scenarios such as robot grasping, augmented reality, and automatic driving. [0003] There are many methods for 6D pose prediction. Deep learning technology can greatly improve the accuracy of 6D pose prediction. With the development of deep learning technology, 6D pose is generally annotated in two ways: one is throug...

AI Technical Summary

Problems solved by technology

[0003] There are many methods for 6D pose prediction. Deep learning technology can greatly improve the accuracy of 6D pose prediction. With the development of deep learning technology, 6D pose is generally annotated in two ways: one is through manual data annotation , because deep learning requires a large amount of data to support, manual labeling of 6D poses often requires a lot of time and workload, which not only results in high labor costs, but also seriously affects the labeling efficiency; the second is to make a large number of The data of the deep learning network is used for training, and then the trained network is predicted for the real scene, which can save the time of manual labeling, but due to the difference between the simulation data and the data in the real scene, the deep learning training results The accuracy of the results cannot be guaranteed

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